Evidence of differences in diurnal electrodermal, temperature and heart rate patterns by mental health status in free-living data

ABSTRACT

Key Aspects

• 🎯 Objective: Real-World EDA Monitoring: The abstract outlines the study's primary objective, which is to address the limitations of laboratory-based research on electrodermal activity (EDA) as a mental health indicator. By leveraging recently developed commercial smartwatches with continuous EDA sensors, the research aims to investigate the association between diurnal physiological patterns and self-reported symptoms of depression, anxiety, and stress. This positions the work as a crucial step in translating psychophysiological research into free-living, real-world contexts, enabling passive and longitudinal monitoring of sympathetic arousal.
• 🔬 Methodology: Wearable Sensing Study: The study employed a prospective, non-randomised design involving 237 participants over a four-week observation period. Data collection was multi-modal, combining passively collected sensor data from a commercial wearable with self-reported mental health measures. The physiological data included EDA, heart rate (HR), heart rate variability (HRV), and skin temperature, which were analyzed using a non-linear cosinor fitting method to model and compare their diurnal rhythms between different mental health status groups.
• 📊 Key Findings: Differentiated Physiological Signatures: The findings reveal distinct physiological patterns correlated with mental health status. Individuals with elevated symptoms of depression and anxiety showed significantly higher tonic EDA, skin temperature, and heart rate compared to control subjects, even when accounting for physical activity. In contrast, elevated stress was only associated with higher skin temperature. The most notable discovery was that the difference in EDA between high- and low-symptom groups was most prominent during the early morning hours, suggesting a specific temporal window of altered sympathetic arousal.
• 🔑 Conclusion: Wearables as Mental Health Tools: The abstract concludes that wearable sensor data can identify different diurnal physiological patterns in individuals with elevated depression and anxiety symptoms. These differences, particularly in EDA, point towards heightened sympathetic nervous system activity throughout the day, with a peak in the early morning. This conclusion supports the clinical potential of consumer smartwatches as practical tools for measuring physiological correlates of mental health symptoms in naturalistic, everyday settings, opening new avenues for research and technology development.

Strengths

• ✅ Clear and Logical Structure

  The abstract is exceptionally well-organized, following the standard Background, Objective, Methods, Findings, and Conclusions format. This logical flow allows readers to quickly grasp the study's rationale, execution, and primary outcomes without ambiguity, making the core information highly accessible.

  "This work (n=237, 4- week observation period) examines the association between passively collected, diurnal variations in EDA and symptoms of depression, anxiety and perceived stress in a large community sample." (Page 1)
• ✅ Highlights Novelty and Contribution

  The abstract effectively communicates the study's primary contribution by contrasting the limitations of previous lab-based studies with the opportunities afforded by new wearable technology. It clearly states how this research adds to the existing knowledge base, emphasizing the novelty of collecting in-situ diurnal EDA data in a free-living context.

  "However, the inability to measure EDA passively over time and in the real world has limited conclusions that can be drawn about EDA as an indicator of mental health status outside of controlled settings." (Page 1)
• ✅ Specific and Impactful Findings

  The findings are presented with precision, identifying not only which physiological markers were elevated (EDA, skin temperature, heart rate) but also for which conditions (depression and anxiety). The specific identification of the 'early morning' as the most prominent period of EDA difference is a particularly strong and impactful finding that points toward specific mechanisms.

  "Most strikingly, differences in EDA between those with high versus low symptoms were most prominent during the early morning." (Page 1)

Suggestions for Improvement

• 💡 Specify the Wearable Device for Context

  Medium impact. The abstract mentions 'Recent smartwatches' and 'wrist- worn continuous EDA sensors' but does not name the specific device used (Fitbit Sense 2). Specifying the device in the abstract's Methods section would enhance transparency and provide crucial context for researchers, as sensor technology and algorithms can vary significantly between manufacturers, affecting the generalizability and reproducibility of the findings.

  "Recent smartwatches have begun to incorporate wrist- worn continuous EDA sensors that enable longitudinal measurement of sympathetic arousal in everyday life." (Page 1)

  Implementation: In the 'Methods' paragraph of the abstract, revise the sentence describing participant recruitment to explicitly name the device. For example, change 'We recruited 395 participants who had a Fitbit Sense 2 device with the electrodermal sensor activated.' to a more concise version integrated earlier, such as in the Objective: '...smartwatches, such as the Fitbit Sense 2, have begun to incorporate...'

• 💡 Quantify the Primary Finding

  High impact. The abstract states that subjects with elevated symptoms had 'higher tonic EDA,' but it lacks a quantitative measure to convey the magnitude or statistical significance of this key finding. Including a primary statistic, such as an effect size or p-value for the difference in EDA, would substantially strengthen the abstract by providing concrete evidence of the effect and allowing readers to more accurately gauge the finding's robustness at a glance.

  "Subjects who exhibited elevated depressive and anxiety symptoms had higher tonic EDA, skin temperature and heart rate, despite not engaging in greater physical activity, compared with those that were not depressed or anxious." (Page 1)

  Implementation: At the end of the sentence reporting the main finding in the 'Findings' section, add the key statistic from the main results (e.g., from Table 1). For example, amend '...had higher tonic EDA, skin temperature and heart rate...' to '...had significantly higher tonic EDA (p < .05), skin temperature, and heart rate...' or include the difference in mesor to provide a measure of effect.

INTRODUCTION

Key Aspects

• 🗺️ Research Context and Technological Opportunity: The introduction defines Electrodermal Activity (EDA) as a key measure of sympathetic arousal used in psychophysiological research. It immediately establishes the central problem: the historical difficulty of measuring EDA in 'free-living' (natural, everyday) contexts has limited its application outside of controlled laboratories. The paper then presents the solution, explaining how recent advances in commercial smartwatches with continuous EDA sensors create an unprecedented opportunity to overcome these limitations and study physiological responses at scale in the real world.
• ⚖️ Competing Theoretical Models: The paper presents conflicting theories regarding how EDA should manifest across different mental health conditions, highlighting a key tension in the literature. One perspective suggests that anxiety, linked to sympathetic hyperactivity, should correlate with elevated EDA. Conversely, another theory posits that depression, characterized by emotional disengagement, might lead to blunted or reduced autonomic responses. This theoretical ambiguity underscores the inconclusive nature of existing evidence and establishes a clear need for empirical clarification through real-world data.
• 📚 Literature Gap and Study Rationale: The authors effectively synthesize existing literature, noting that while some studies link EDA to mental health, they are predominantly lab-based and rely on contrived stimuli. The text explicitly states that 'In-situ observations of diurnal variations in EDA are extremely limited,' especially for psychiatric subgroups. This clearly identified gap provides a powerful rationale for the current study, which aims to generate this crucial real-world evidence and contextualize EDA findings with other physiological measures like heart rate and skin temperature.
• ⚙️ Methodological Rationale for Analysis: The authors demonstrate methodological foresight by addressing the significant comorbidity and conceptual overlap between stress, anxiety, and depression. They explain that these constructs share common underlying processes, such as negative affectivity, and that statistically controlling for one while examining another could distort the results by removing meaningful shared variance. This justification explains the decision to model each condition separately, a choice that aims to enhance the study's ecological validity and the interpretability of its findings.
• ❓ Specific Study Hypotheses: Drawing from the literature review, the introduction culminates in a clear and testable hypothesis for the study. The authors predict that individuals with elevated symptoms of mental health conditions will exhibit higher overall levels of EDA. Furthermore, they hypothesize that an analysis of diurnal (24-hour) patterns will reveal that these physiological differences are most pronounced during the early morning, pointing toward a specific temporal window of interest for altered sympathetic arousal.

Strengths

• ✅ Establishes a Clear Research Gap

  The introduction excels at framing the study's importance by contrasting the potential of Electrodermal Activity (EDA) as a biomarker with the historical inability to measure it in naturalistic settings. It clearly articulates that previous research is limited to laboratory environments, creating a compelling justification for the current study's use of novel wearable technology to fill this critical gap.

  "However, the measurement of EDA in free- living contexts has been a longstanding challenge due to the paucity of convenient, unobtrusive and comfortable wearable measurement solutions." (Page 1)
• ✅ Synthesizes Competing Theories

  The authors skillfully present the theoretical ambiguity in the field. They outline competing hypotheses—one predicting elevated EDA in anxiety due to sympathetic hyperactivity and another suggesting blunted responses in depression from emotion disengagement—which effectively highlights the need for the empirical data this study provides to resolve these inconclusive findings.

  "Other work would suggest that emotion disengagement and decreased motivation would lead to blunted autonomic responses in a depressed population." (Page 2)
• ✅ Proactively Addresses Methodological Challenges

  The introduction demonstrates strong methodological rigor by prospectively addressing the issue of high comorbidity among depression, anxiety, and stress. The explicit rationale for analyzing these constructs separately, to avoid removing meaningful shared variance, shows a sophisticated understanding of psychometrics and enhances confidence in the study's design and the interpretability of its findings.

  "Because of this high interrelatedness, statistically controlling for stress when examining anxiety or depression may inadvertently remove meaningful shared variance, leaving only unique residual variance that no longer accurately represents the original constructs." (Page 2)

Suggestions for Improvement

• 💡 Broaden Hypotheses to Include Ancillary Measures

  High impact. The introduction effectively establishes the importance of ancillary measures like heart rate and skin temperature and reviews their connection to depression. However, the formal hypothesis at the end of the section only mentions EDA. Explicitly including directional hypotheses for heart rate and skin temperature would better align the introduction with the study's full scope as presented in the methods and results, creating a more cohesive and comprehensive narrative arc for the reader.

  "We include analysis of these ancillary peripheral physiological measures of sympathetic autonomic nervous system arousal to provide more context to the SCL findings." (Page 2)

  Implementation: Revise the final paragraph of the introduction to incorporate the other measures into the hypothesis. For example, after the sentence on EDA, add: 'Furthermore, based on prior findings, we also hypothesized that these groups would exhibit higher resting heart rate and skin temperature, providing convergent evidence of heightened sympathetic arousal.'

• 💡 Briefly Justify Focus on Tonic EDA

  Medium impact. The introduction states that 'Tonic changes in the EDA signal—represented by the skin conductance level (SCL)—have been reported as a useful feature.' While correct, a brief clause explaining why tonic EDA (a measure of baseline arousal) is particularly well-suited for this type of free-living, ambulatory study compared to phasic EDA (rapid, event-related responses) would strengthen the rationale. This would clarify for readers less familiar with EDA why SCL is the primary metric, especially given the challenges of ambulatory measurement.

  "Tonic changes in the EDA signal—represented by the skin conductance level (SCL)—have been reported as a useful feature for evaluating sympathetic arousal." (Page 1)

  Implementation: In the first paragraph of the introduction, modify the sentence about tonic changes to provide this context. For example: 'Tonic changes in the EDA signal—represented by the skin conductance level (SCL)—have been reported as a useful feature for evaluating sympathetic arousal, as they reflect baseline levels that can be more robustly measured in ambulatory settings than rapid, event-related phasic responses.'

METHODS

Key Aspects

• 🗺️ Study Design and Recruitment: The study employed a prospective, non-randomised design to observe participants in their natural environment over a four-week period. Data was collected via passive sensing from a commercially available wearable (Fitbit Sense 2) and self-report surveys. Recruitment targeted a broad population of US adult Android users through the Fitbit application, aiming to capture a wide range of mental health experiences outside of a controlled laboratory setting. This design is significant for its ecological validity, allowing for the investigation of physiological patterns as they occur in daily life.
• ⚖️ Participants and Ethical Considerations: The study included 237 participants who completed all intake surveys, exhibiting a range of depression, anxiety, and stress symptoms representative of a broad community sample. The methodology placed a strong emphasis on ethical data handling, including obtaining detailed informed consent, de-identifying all personal data according to HIPAA standards, and providing participants with full control over sensor activation and data deletion. This ethical framework is crucial for building trust and ensuring participant protection in digital health studies involving sensitive personal and physiological information.
• ⌚ Sensor Technology and Data Acquisition: The primary instrument was the Fitbit Sense 2 smartwatch, which features a continuous electrodermal activity (EDA) sensor on the dorsal (top) side of the wrist. This sensor measures skin impedance, which is then processed into minute-level admittance values, a measure of skin conductance. The focus on tonic EDA (skin conductance level, or SCL) is a key methodological choice, as it reflects baseline sympathetic arousal and is more robust to motion artifacts common in ambulatory settings compared to rapid, event-related phasic responses. The device also collected ancillary data from optical heart rate, skin temperature, and motion sensors.
• 📊 Data Processing and Signal Filtering: Raw sensor data underwent a rigorous filtering and aggregation process to ensure quality and prepare it for statistical modeling. Spurious measurements were removed by applying physiological range filters to skin conductance level (0-30 µS) and heart rate (40-250 bpm). The data was then aggregated by participant and by hour to generate diurnal (24-hour) patterns. A notable feature of the data collection is that the SCL sensor was deactivated during sleep to conserve battery, a practical constraint that influences data availability during nighttime hours and is important for interpreting the results.
• 💻 Statistical Modeling Approach: A dual-pronged statistical approach was used to analyze the data. First, a non-linear cosinor fitting method (CircaCompare2) was employed to model the diurnal rhythms of the physiological signals and test for differences in their core properties—mesor (rhythm-adjusted mean), amplitude, and phase—between mental health groups. Second, to account for potential confounding demographic variables not handled by the cosinor model, a separate linear model using ordinary least squares (OLS) regression was fitted. This two-model strategy allows for both a detailed characterization of rhythmic patterns and a robust, controlled examination of the relationships between physiological measures and mental health scores.

Strengths

• ✅ Excellent Transparency in Data Processing

  The methodology demonstrates a high degree of transparency by explicitly stating the parameters used for data processing, such as the filtering ranges for SCL and heart rate, and the aggregation of data into hourly blocks. This clarity is essential for the scientific community as it directly supports the reproducibility and critical evaluation of the study's findings.

  "The SCL values were filtered between 0 and 30 microsiemens, the HR between 40 and 250 beats/min, skin temperatures and steps were not filtered. The values were then aggregated by participant and hour to generate daily patterns from midnight to midnight." (Page 3)
• ✅ Robust Dual-Model Statistical Analysis

  The use of a two-pronged statistical approach is a significant strength. Employing a non-linear cosinor model to analyze diurnal rhythms and a separate linear regression model to control for demographic covariates shows a sophisticated understanding of the data's complexities and the limitations of individual statistical methods, thereby strengthening the validity of the conclusions.

  "The CircaCompare package does not handle additional covariates; therefore, to investigate whether the differences in mesors were a result of differences in population demographics, we used a linear model fit via ordinary least squares regression..." (Page 3)
• ✅ Detailed Sensor and Measurement Specification

  The authors provide commendable detail on the sensor technology, including the raw signal acquisition rate (200 Hz), subsequent downsampling, and the conversion from impedance to admittance. This technical specificity is crucial for interpreting the results, comparing them with studies using different hardware, and understanding the precise nature of the physiological signal being analyzed.

  "This impedance signal is acquired at 200 samples-per-second, which is then passed through a boxcar filter and downsampled to 25 samples-per-second." (Page 3)

Suggestions for Improvement

• 💡 Justify the Choice of Signal Filtering Thresholds

  High impact. The methods clearly state the filtering ranges for SCL and HR, which is a strength. However, providing a brief rationale for these specific thresholds would significantly enhance methodological transparency and rigor. Explaining whether these values are derived from established literature, manufacturer guidelines, or an empirical analysis of the dataset's distribution would help readers assess the potential impact of this filtering step on the results and improve the study's reproducibility.

  "The SCL values were filtered between 0 and 30 microsiemens, the HR between 40 and 250 beats/min, skin temperatures and steps were not filtered." (Page 3)

  Implementation: In the paragraph describing signal filtering, add a sentence or clause explaining the basis for the chosen ranges. For example: "The SCL values were filtered between 0 and 30 microsiemens, a range consistent with established literature on ambulatory human skin conductance, to remove non-physiological artifacts..."

• 💡 Explain the Rationale for Not Filtering Skin Temperature

  Medium impact. The manuscript explicitly states that skin temperature and step data were not filtered, in contrast to SCL and HR data. While the need to filter SCL and HR is intuitive for removing artifacts, the decision to leave skin temperature unfiltered could be clarified. Briefly explaining the rationale—for example, that the sensor's output is less prone to the types of high-frequency noise that affect SCL, or that the full range of values was considered physiologically plausible—would strengthen the methodological description and preempt reader questions about inconsistent data handling across different sensor streams.

  "The SCL values were filtered between 0 and 30 microsiemens, the HR between 40 and 250 beats/min, skin temperatures and steps were not filtered." (Page 3)

  Implementation: In the sentence on filtering, add a brief parenthetical or clause explaining the decision. For example: "...skin temperatures and steps were not filtered, as their raw values were determined to fall within a physiologically plausible range with minimal high-frequency artifacts."

RESULTS AND DISCUSSION

Key Aspects

• 📈 Primary Finding: Elevated Skin Conductance in Depression: The study's central finding is that individuals with depressive symptoms exhibit significantly elevated tonic skin conductance levels (SCL), a physiological indicator of sympathetic nervous system arousal. This result is robustly supported by two distinct statistical models: a non-linear cosinor model analyzing diurnal rhythms (Table 1) and an Ordinary Least Squares (OLS) model that controls for demographic factors (Table 2). The data, visualized in Figure 1, indicates this elevation is particularly prominent during the early morning and daytime hours, suggesting a persistent state of heightened arousal.
• ⚖️ Dissociation from Physical Activity: A crucial aspect of the analysis is the finding that the observed increase in sympathetic arousal (SCL) in the depressed group is not a byproduct of increased physical activity. The authors explicitly note that this group did not take more steps than the control group. This dissociation is significant because it strengthens the interpretation that the elevated SCL is driven by internal, psychological, or neurophysiological factors related to depression itself, rather than being a secondary effect of behavior.
• 🌍 Validation Against Population Norms: To establish a credible baseline, the study compares the diurnal patterns of its control group (low depression, anxiety, and stress) with data from a much larger, previously reported sample of over 15,000 Fitbit users (Figure 2). The close resemblance between the study's control group and the general population, especially for SCL and heart rate, provides strong validation. This demonstrates that the study's control group is representative of a normative physiological state, thereby increasing confidence that the observed differences in the symptomatic groups are meaningful deviations.
• ⚕️ Ancillary Physiological Correlates: Beyond SCL, the study examines other physiological variables to provide a more comprehensive picture of autonomic function. The results confirm prior research, showing that the depressed group also had slightly elevated skin temperature and higher heart rates. However, the authors note that these effects were smaller than those for SCL and, in the case of heart rate, lost significance after controlling for demographics in the OLS model. This multi-modal analysis adds context and suggests that SCL may be a more robust and specific ambulatory marker for the physiological signature of depression compared to these other measures.
• 🔑 Significance: A Measurable Signal for Autonomic Dysfunction: The discussion section synthesizes the findings, arguing that the observed physiological patterns are not novel discoveries of autonomic dysfunction in mood disorders, which is well-documented. Instead, the study's unique contribution is demonstrating that these abnormalities, linked to systems like the hypothalamic–pituitary axis, produce a 'consistent and discernible signal' that can be captured by a commercial wearable device in a real-world setting. This makes the long-observed physiological correlates of mental illness measurable and potentially actionable at a population scale, opening new avenues for predictive and personalized mental healthcare.
• ❓ Acknowledged Limitations and Confounds: The authors transparently discuss several limitations that temper the interpretation of the results. These include the high comorbidity between anxiety and depression, making it difficult to isolate specific effects, and the relatively mild symptom severity of the participants, which might mask more pronounced differences. Critically, they also highlight technical constraints, such as the SCL sensor being deactivated during sleep to conserve battery, which increases measurement uncertainty during late-night and early-morning hours, and the susceptibility of ambulatory sensors to noise from movement or environmental factors like water.

Strengths

• ✅ Robust Contextualization with Population Data

  The paper significantly strengthens its findings by comparing the study's control group to a massive, independent dataset of over 15,000 users. This step effectively validates their baseline, showing it aligns with general population norms, which increases confidence that the observed differences in the symptomatic groups are genuine and not an artifact of their specific sample.

  "The control group (no depression, no anxiety, low stress) diurnal data in our study resemble the population level traces closely, particularly for SCL and heart rate measurement." (Page 3)
• ✅ Proactive Control for Behavioral Confounders

  The authors demonstrate strong methodological rigor by explicitly testing and discussing the influence of physical activity. By showing that elevated SCL in the depressed group occurred despite no corresponding increase in step count, they effectively rule out a major potential confound and strengthen the argument that the observed sympathetic arousal is linked to internal state rather than behavior.

  "Elevated tonic SCL in the depressed group is observed despite their taking no more steps, suggesting that the increased sympathetic arousal may be driven by factors other than physical activity." (Page 3)
• ✅ Sophisticated Interpretation of Clinical Significance

  The discussion moves beyond a simple reporting of results to provide a sophisticated interpretation of their meaning for the field. It correctly frames the study's primary contribution not as discovering a link between autonomic activity and depression, but as demonstrating that this link can be reliably measured at scale with consumer technology, making it an 'actionable for predictive decision making at scale.'

  "What is unique in the current work is a population profile of the physiology associated with autonomic activity as a consistent and discernible signal of those abnormal rhythms, making them measurable and actionable for predictive decision making at scale." (Page 5)

Suggestions for Improvement

• 💡 Reconcile 'Early Morning' Claims with Data Limitations

  High impact. The discussion repeatedly emphasizes that SCL differences are most prominent in the 'early morning/night time hours.' However, the limitations section and Figure 1 caption explicitly state that the SCL sensor was turned off during sleep and data from 01:00-06:00 has low confidence. This creates an apparent contradiction that could undermine a key finding. The discussion needs to explicitly address this, clarifying whether 'early morning' refers to the period immediately upon waking (e.g., 06:00-08:00) where data is more reliable, or by providing a more nuanced interpretation that acknowledges the data gap.

  "The plots illustrate that SCL is elevated, particularly in the depressed group and most prominently in the early morning/night time hours." (Page 3)

  Implementation: In the first paragraph of the Results and Discussion, after mentioning the early morning hours, add a sentence to clarify the temporal window in light of the sensor's sleep deactivation. For example: 'While the sensor was deactivated for much of the typical sleep period (approx. 22:00-06:00), these differences were most apparent in the hours immediately following this period, from 06:00 to noon, suggesting a heightened sympathetic arousal upon waking.'

• 💡 Explore the Divergence Between Depression and Anxiety Findings

  High impact. The paper notes high comorbidity and expects 'similarity in the diurnal patterns.' Yet, a key finding is that while SCL is elevated in the depression group, it was not significantly greater for the anxiety group. This is a potentially important differentiating result that is currently under-discussed. The discussion would be significantly strengthened by exploring potential reasons for this divergence, connecting it to literature that may distinguish the physiological signatures of generalized anxiety from depression in ambulatory settings.

  "However, tonic SCL was not significantly greater for the anxious group, consistent with prior work analysing responses in lab conditions." (Page 5)

  Implementation: Add a new paragraph to the discussion section dedicated to this finding. It could begin: 'A noteworthy finding was the divergence in SCL patterns between the depression and anxiety groups, despite their high comorbidity. While prior lab-based work has shown mixed results for EDA in GAD, our findings suggest that in a free-living context, tonic SCL may be a more specific marker of depressive states. This could reflect differing underlying mechanisms, such as the blunted emotional reactivity sometimes associated with depression versus the cognitive worry of anxiety, which may not manifest as persistently elevated tonic arousal.'

• 💡 Address the Impact of High Comorbidity on the 'Depression' Group

  Medium impact. The analysis treats the depression, anxiety, and stress groups separately. However, given the high comorbidity acknowledged in the text and supplemental materials, the 'depression group' likely contains a large proportion of individuals who also have anxiety. The discussion should explicitly consider whether the elevated SCL signature is truly specific to depression, or if it reflects the physiology of a comorbid depression-anxiety state, which may be distinct from anxiety alone.

  "As expected, and consistent with high rates of comorbidity in the literature, in this population there is a considerable overlap in subjects who were depressed and anxious in particular..." (Page 5)

  Implementation: In the paragraph discussing the comorbidity, expand on the implications. For example: 'Given the substantial overlap between depressed and anxious subjects in our cohort, it is important to consider that the elevated SCL observed in the depression group may reflect the signature of a comorbid state rather than depression in isolation. Future work with larger, more clinically distinct samples would be needed to fully disentangle the unique physiological contributions of each condition.'

Non-Text Elements

Figure 1. Diurnal patterns in physiological and behavioural measures across the...

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Figure 1. Diurnal patterns in physiological and behavioural measures across the data set.

Figure/Table Image (Page 4)

First Reference in Text

Figure 1 shows average HR, HRV RMSSD, SCL, skin temperature and step counts as a function of the depression, anxiety and stress groupings.

Description

• Data availability varies significantly throughout the day: This line graph illustrates data availability across a 24-hour cycle. The number of participants providing sensor data is lowest during the night, dropping below 1,000 observations between approximately 01:00 and 06:00. Data availability rises sharply in the morning and remains high throughout the day, peaking at approximately 2,800 observations around 20:00.

Scientific Validity

• ✅ Transparently displays data availability: Presenting the data availability is a commendable act of transparency. It allows the reader to critically assess the robustness of the findings presented in panel B, particularly during the early morning hours where data is sparse. This is crucial for interpreting the diurnal patterns correctly.

Communication

• ✅ Effective use of shading to indicate data sparsity: The grey shaded area from 01:00 to 06:00 is an excellent visual device to explicitly denote the period of low data availability and thus lower confidence in the observed patterns. This directly links to the information presented in panel B, enhancing the overall coherence of the figure.
• 💡 Y-axis label is unclear: The y-axis label, 'No. Participant Minutes,' is ambiguous. The caption clarifies that the total possible is 6636 (28 days × 237 participants), suggesting the y-axis represents the total number of participant-days with data available at each specific minute of the day. A more intuitive label, such as 'Number of Daily Observations' or 'Total Participant-Days with Data,' would improve clarity and reduce potential misinterpretation.
• 💡 Missing panel label: The plot lacks a panel label (e.g., 'A') to clearly distinguish it from the grid of plots below it (panel B), as referenced in the caption. Adding a label would improve navigational clarity within the multi-pane figure.

Table 1 Non-linear function (CircaCompare²) outputs when comparing not...

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Table 1 Non-linear function (CircaCompare²) outputs when comparing not depressed (PHQ-8 <5) and mildly depressed or depressed groups (PHQ- 8 ≥5), not anxious (GAD-7 <5) and mildly anxious or anxious groups (GAD-7 ≥5), and low stress (PSS <14) and moderate-to-high stress (PSS ≥14) groups.

Figure/Table Image (Page 5)

First Reference in Text

In our cohort of 237 subjects, we observe elevated SCL levels in the depression (vs low depression) group (see table 1 for cosinor fitting results).

Description

• Overview of the table's purpose and methodology: This table presents a statistical comparison of daily biological rhythms between individuals with low versus high symptoms of depression (PHQ-8), anxiety (GAD-7), and stress (PSS). The analysis breaks down each 24-hour rhythm into three key components: the mesor (the rhythm's average value), the amplitude (the size of the daily fluctuation), and the phase (the timing of the daily peak).
• Key findings for depression (PHQ-8): For individuals with higher depression symptoms (PHQ-8 ≥5), the analysis found statistically significant increases in the average daily level (mesor) of Skin Conductance Level (SCL), a measure of sweat activity, by 0.73 units; skin temperature by 0.12°C; and heart rate by 2.81 beats per minute, compared to the non-depressed group. No significant differences were found in the amplitude or timing of these rhythms.
• Key findings for anxiety (GAD-7): Similarly, individuals with higher anxiety symptoms (GAD-7 ≥5) showed significantly higher average daily SCL (an increase of 0.70 units), skin temperature (an increase of 0.20°C), and heart rate (an increase of 2.41 beats per minute) compared to the non-anxious group.
• Key findings for stress (PSS): The differences for perceived stress were not statistically significant at the p<0.05 level. The only notable finding was a borderline increase in the average daily skin temperature for the high-stress group (an increase of 0.09°C), which had a p-value less than 0.1.

Scientific Validity

• ✅ Appropriate statistical method for rhythmic data: The use of cosinor analysis is highly appropriate for the research question, as it allows for a nuanced examination of diurnal patterns beyond simple daily averages. Decomposing the rhythms into mesor, amplitude, and phase provides a more detailed understanding of how physiological patterns differ between groups.
• ✅ Inclusion of confidence intervals enhances rigor: The inclusion of 95% confidence intervals for every estimate is a methodological strength. It provides readers with a clear sense of the precision of the estimates and the potential range of the true effect sizes, which is more informative than p-values alone.
• 💡 Potential issue with multiple comparisons: The table presents the results of 45 independent statistical tests (5 variables x 3 parameters x 3 conditions). There is no mention of an adjustment for multiple comparisons (e.g., Bonferroni correction, FDR). Without such an adjustment, the risk of finding a significant result by chance (a Type I error) is inflated. The authors should address this, either by applying a correction or by justifying its omission.
• 💡 Lack of exact p-values: The table does not report exact p-values, instead using bolding for p<0.05 and a symbol for p<0.1. While this indicates significance, providing exact p-values would offer more granular information, distinguishing between results that are marginally significant (e.g., p=0.049) and those that are highly significant (e.g., p<0.001).
• 💡 Potential for Type II errors due to low statistical power: The wide confidence intervals for many of the non-significant parameters (e.g., the difference in HRV RMSSD mesor for PHQ-8 is -13.79 to 7.19) suggest that the study may have been underpowered to detect smaller, yet potentially meaningful, differences. The authors should be cautious in their interpretation, framing these as 'no detected difference' rather than 'no difference'.

Communication

• ✅ Clear and logical structure: The table is well-organized with a clear hierarchical structure, grouping results by mental health condition (PHQ-8, GAD-7, PSS), rhythmic parameter, and physiological variable. This logical layout allows readers to efficiently locate and compare specific findings.
• ✅ Effective use of bolding for significance: Using bold text to highlight statistically significant results (p<0.05) is an effective visual cue that immediately draws the reader's attention to the most important findings of the analysis.
• 💡 Over-reliance on abbreviations and symbols: The table relies heavily on abbreviations (SCL, HRV RMSSD) and symbols for cosinor parameters (k1, alpha1, psi1) that may not be intuitive. While a footnote defines the mental health scales, adding parenthetical explanations within the table itself (e.g., 'Diff. in mesor (average)', 'SCL (Skin Conductance)') would greatly improve its self-contained clarity and readability without requiring the reader to hunt for definitions.
• 💡 Redundant column headers could be streamlined: The column headers 'Est.' and '95% CI' are repeated for each of the five physiological measures, creating visual redundancy. Consider using a multi-level header where each measure (e.g., 'SCL', 'Skin temp.') spans two sub-columns for 'Estimate' and '95% CI'. This would streamline the table's appearance and reduce clutter.

Table 2 Fixed effect estimates, SEs and estimated p values from the linear...

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Table 2 Fixed effect estimates, SEs and estimated p values from the linear mixed effects model

Figure/Table Image (Page 5)

First Reference in Text

Based on figure 1 differences appear greatest from 06:00 to noon; however, due to the smaller number of samples during the 06:00-07:00 period, there is also larger variance, Differences in SCL during the night-time/early morning hours between people with no depressive/anxiety symptoms and elevated depressive/ anxiety symptoms deserve further research as our results suggest there might be phenotypical differences. Elevated tonic SCL in the depressed group is observed despite their taking no more steps, suggesting that the increased sympathetic arousal may be driven by factors other than physical activity. For context, figure 2 shows a comparison between the groups in our study and baseline diurnal pattern from a sample of 15349 Fitbit Sense 2 users (for whom there is no survey data) reported in prior work. The control group (no depression, no anxiety, low stress) diurnal data in our study resemble the population level traces closely, particularly for SCL and heart rate measurement. Our results do not support theories that electrodermal hypore- activity is a feature of depression in all contexts. Differences in physiological diurnal patterns were observed when comparing PHQ score, GAD score and PSS score in our cohort of 237 subjects. As expected, and consistent with high rates of comorbidity in the literature, in this population there is a considerable overlap in subjects who were depressed and anxious in particular (see covariance matrix in online supple- mental table S2) and therefore similarity in the diurnal patterns is unsurprising. However, tonic SCL was not significantly greater for the anxious group, consistent with prior work analysing responses in lab conditions. When we consider the other physiological variables, we see patterns that confirm results in prior work. Skin temperature was slightly elevated in the depressed group, consistent with a recent large study in ambulatory settings of 20000 subjects. The result was consistent in the cosinor model and the OLS model; however, the effects were smaller than for SCL. Heart rate was higher in the depressed group in the cosinor model; however, the tonic effect was no longer significant when controlling for other variables in the OLS model. Table 2 Fixed effect estimates, SEs and estimated p values from the linear mixed effects model Predictor variable PHQ-8 GAD-7 PSS Intercept 3.41 4.51 13.9 Demographics Age -0.83 -1.15 -1.43 Weight 0.47 0.002 -0.55 Gender (female) 1.57 0.94 1.88 Orientation (LGBTQI+) 3.38 1.09 2.34 Race (not Caucasian) -0.81 -0.93 -0.44 Phys./behaviour Mean tonic SCL 0.23 0.03 0.24 Mean HR 0.06 0.09 0.39 Mean HRV RMSSD -0.15 -0.28 -0.63 Mean skin temp. 0.05 0.12 0.06 Mean steps -0.09 0.13 0.02 Bold values significant to p<0.01. p<0.01. GAD, Generalised Anxiety Disorder; HR, Heart Rate; HRV, Heart Rate Variability; LGBTQI+, Lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other related identities.; PHQ, Patient Health Questionnaire; PSS, Perceived Stress Scale; RMSSD, root mean square of successive differences; SCL, skin conductance level.

Description

• Overview of the statistical model and its outputs: This table presents the results of a linear mixed effects model, a statistical analysis designed to understand the relationship between various factors and mental health scores in a group of individuals over time. The numbers in the table, called 'fixed effect estimates', show how much a mental health score (for depression, anxiety, or stress) is predicted to change for every one-unit increase in a given predictor, after accounting for all other factors in the model.
• Key physiological predictors of depression scores: For depression (PHQ-8), higher scores are significantly associated with higher mean tonic Skin Conductance Level (SCL), which measures sweat activity as an indicator of arousal (coefficient = 0.23), and higher mean heart rate (0.06). Conversely, higher Heart Rate Variability (HRV RMSSD), a measure of beat-to-beat changes in heart rate often linked to better stress regulation, is associated with lower depression scores (-0.15).
• Key physiological and behavioral predictors of anxiety scores: For anxiety (GAD-7), several factors show a significant association. Notably, higher mean heart rate (0.09), higher skin temperature (0.12), and more steps taken (0.13) are linked to higher anxiety scores. In contrast, higher HRV RMSSD is associated with lower anxiety scores (-0.28).
• Key physiological and demographic predictors of stress scores: For stress (PSS), the model shows that higher mean tonic SCL (0.24) is linked to higher stress scores, while higher HRV RMSSD is associated with lower stress scores (-0.63). Among demographics, being female (1.88) and identifying as LGBTQI+ (2.34) were associated with higher stress scores, while older age (-1.43) and higher weight (-0.55) were associated with lower scores.

Scientific Validity

• ✅ Appropriate statistical model for longitudinal data: The use of a linear mixed effects model is a significant strength. This approach is well-suited for longitudinal data as it correctly accounts for the non-independence of repeated measurements within the same individual, providing more accurate and reliable estimates than a standard regression model.
• ✅ Comprehensive model controls for multiple confounders: The model simultaneously includes demographic, physiological, and behavioral predictors. This multivariate approach allows for the examination of the unique contribution of each variable while controlling for others, which is a more rigorous method than the univariate comparisons presented in Table 1.
• 💡 Inconsistency in the description of the statistical model: The reference text mentions using an 'OLS model', while the methods section and this table's caption refer to a 'linear mixed effects model'. These are different statistical techniques. This inconsistency should be resolved to ensure clarity and accuracy regarding the methodology used.
• 💡 Potential for inflated Type I error due to multiple testing: The table reports coefficients for multiple predictors for three different outcomes. Given the number of statistical tests performed, there is an increased risk of Type I errors (false positives). The authors should clarify whether any correction for multiple comparisons was applied or provide a justification for its absence.

Communication

• ✅ Logical grouping of predictor variables: The table's structure is clear, with predictor variables logically grouped into 'Demographics' and 'Phys./behaviour'. This organization makes it easy for the reader to navigate the results and compare the effects of different types of variables.
• 💡 Caption is inconsistent with table content: The caption is inconsistent with the table's content. It states that the table includes 'SEs and estimated p values', but neither standard errors nor exact p-values are presented. The table only shows the coefficient estimates and uses an asterisk to indicate significance. The caption should be revised to accurately describe the contents.
• 💡 Conflicting significance indicators in the footnote: The footnote is confusing as it provides two different indicators for the same significance level: 'Bold values significant to p<0.01' and '*p<0.01'. Since only asterisks are used in the table body, the reference to bold values should be removed to avoid ambiguity and improve clarity.
• 💡 Missing units for predictor variables: The units of the predictor variables are not provided. For example, it is unclear if physiological measures are in their raw units or are standardized (Z-scored, as mentioned in the methods). This omission makes it difficult to interpret the practical magnitude of the coefficients. Adding a note about units or standardization would significantly improve the table's interpretability.

Figure 2 Comparison between study diurnal patterns (n=237) and population...

Full Caption

Figure 2 Comparison between study diurnal patterns (n=237) and population diurnal patterns (n=15349).

Figure/Table Image (Page 6)

First Reference in Text

Based on figure 1 differences appear greatest from 06:00 to noon; however, due to the smaller number of samples during the 06:00-07:00 period, there is also larger variance, Differences in SCL during the night-time/early morning hours between people with no depressive/anxiety symptoms and elevated depressive/ anxiety symptoms deserve further research as our results suggest there might be phenotypical differences. Elevated tonic SCL in the depressed group is observed despite their taking no more steps, suggesting that the increased sympathetic arousal may be driven by factors other than physical activity. For context, figure 2 shows a comparison between the groups in our study and baseline diurnal pattern from a sample of 15349 Fitbit Sense 2 users (for whom there is no survey data) reported in prior work.

Description

• Overview of the multi-panel comparison: This figure displays five line graphs, each showing the 24-hour pattern of a different physiological or behavioral measure: Heart Rate, Heart Rate Variability (HRV RMSSD), Skin Conductance Level (SCL), Skin Temperature, and Steps per Minute. It compares three groups: a large general population sample (n=15,349), and two subgroups from the main study (n=237)—those without depression and those with depression.
• Elevated physiological arousal in the depression group: The group with depression consistently shows higher heart rate, higher skin temperature, and higher Skin Conductance Level (SCL) throughout the day compared to the non-depressed group and the general population. SCL, a measure of sweat gland activity that reflects sympathetic nervous system arousal, shows a particularly pronounced difference in the morning hours, where the depressed group's SCL is around 4-5 µSiemens, while the other groups are closer to 2-3 µSiemens.
• Reduced Heart Rate Variability in the depression group: The depressed group exhibits lower Heart Rate Variability (HRV RMSSD) across the 24-hour period. HRV RMSSD measures the tiny variations in time between consecutive heartbeats and is often considered an indicator of the body's ability to adapt to stress. The depressed group's HRV is consistently about 10 ms lower than the non-depressed group's.
• Similar physical activity levels between study groups: Despite the clear differences in physiological measures, the daily pattern of physical activity, measured as steps per minute, is nearly identical for the depressed and non-depressed groups within the study. This suggests that the observed physiological differences are not simply due to one group being more physically active than the other.
• Study's control group is representative of the general population: The physiological patterns of the 'No Depression' study group closely mirror those of the much larger 'Gen. Population' sample across all metrics. This indicates that the non-depressed participants in the study are representative of the broader population, strengthening their validity as a control group.

Scientific Validity

• ✅ Use of a large population sample for context: Including a very large (n=15,349) general population sample as a baseline is a significant methodological strength. It provides a robust, real-world context for the study's findings and increases confidence that the 'No Depression' group is a suitable control.
• ✅ Effectively controls for physical activity as a confounder: By presenting physiological data alongside behavioral data (steps), the figure strongly supports the paper's argument that the elevated sympathetic arousal in the depressed group is not merely a byproduct of different physical activity levels. This is a crucial control that strengthens the study's conclusions.
• 💡 Lack of uncertainty intervals: The figure presents only the mean trend lines without any indication of variance or uncertainty (e.g., shaded 95% confidence intervals or standard error bands). Without this information, it is impossible to visually assess the statistical significance of the differences between the groups. The observed gaps could fall within the natural variability of the data.
• 💡 Unknown composition of the general population sample: The reference text states the 'Gen. Population' group has no associated survey data. This is a limitation, as its mental health composition is unknown. It likely includes individuals with varying levels of depression and anxiety, which would mean the true physiological profile of a 'healthy' population might differ from what is shown. This makes the comparison between the 'Depression' group and the general population less direct than it appears.

Communication

• ✅ Consistent and clear color coding: The use of a consistent and distinct color scheme for the three groups across all five panels is highly effective. It allows for quick and intuitive comparison of the different physiological measures for the same group.
• ✅ Effective multi-panel grid layout: The grid layout, where each physiological measure is plotted in a separate panel against a common time axis, is an excellent choice for presenting this complex dataset. It facilitates easy comparison of the diurnal patterns across different metrics.
• 💡 Legend could be more informative: The legend is clear but could be enhanced by including the sample sizes for each group (e.g., 'Gen. Population (n=15,349)', 'No Depression (n=...)', 'Depression (n=...)'). This would make the figure more self-contained and immediately highlight the vast difference in scale between the population and study samples.
• 💡 Acronyms are not defined in the caption: While the caption is informative, it omits the full name for 'RMSSD'. To improve the figure's self-sufficiency, spell out key acronyms either in the caption or as a footnote, for instance, 'Root Mean Square of Successive Differences (RMSSD)'.

CONCLUSION

Key Aspects

• 🔑 Summary of Core Findings: The conclusion begins by synthesizing the study's primary outcomes, restating that individuals with elevated depressive and anxiety symptoms displayed higher tonic electrodermal activity (EDA), skin temperature, and heart rate compared to controls, even without differences in physical activity. It precisely differentiates these findings from the stress cohort, where only elevated skin temperature was observed. This comprehensive summary effectively recaps the key physiological signatures identified in the research, providing a clear and concise overview of the main results.
• ⏰ Emphasis on Temporal Differences: A central point emphasized in the conclusion is the specific temporal nature of the physiological differences. The text explicitly identifies electrodermal activity (EDA) measurements in the 'early morning' as the most significant point of divergence between the symptomatic and healthy control groups. This highlights a specific window of altered sympathetic arousal, suggesting that the transition from sleep to wakefulness may be a particularly sensitive period for detecting physiological markers of mental health status, a key insight derived from the diurnal analysis.
• 🛠️ Statement of Practical Utility: The conclusion culminates in a powerful statement about the study's practical significance and contribution to the field of digital mental health. It posits that ambulatory skin conductance level (SCL) measured from a consumer smartwatch is a 'practical and useful' method for assessing the severity of mental health symptoms in naturalistic, 'free-living' environments. This final assertion moves beyond the specific data to argue for the broader utility of this technology, positioning it as a viable tool for future research and potential clinical applications.

Strengths

• ✅ Effective and Concise Synthesis of Findings

  The conclusion provides an exemplary synthesis of the study's complex findings, distilling them into a clear and accessible summary. It effectively communicates the primary physiological differences (EDA, skin temperature, heart rate) and correctly attributes them to the specific mental health cohorts (depression/anxiety vs. stress), which is a hallmark of a well-written concluding section.

  "Overall, results indicate that participants with elevated depressive and anxiety symptoms had higher tonic EDA, skin temperature and heart rate, despite not engaging in greater physical activity, compared with those who were not depressed or anxious." (Page 6)
• ✅ Precise Differentiation Between Conditions

  The authors demonstrate precision by not overgeneralizing their results. The conclusion explicitly differentiates the physiological signature of depression and anxiety from that of stress, noting that the latter was only associated with higher skin temperature. This nuance is critical for accurate interpretation and is presented with excellent clarity.

  "In contrast, participants with elevated stress only had higher skin temperature." (Page 6)
• ✅ Strong Statement on Practical Utility

  The final sentence provides a powerful and impactful take-home message that directly addresses the study's broader implications. By stating that ambulatory SCL is 'practical and useful,' the conclusion successfully translates the research findings into a statement of real-world utility, offering a clear value proposition for clinicians and researchers in digital mental health.

  "Our results suggest ambulatory SCL measured from the dorsal wrist can be practical and useful in measuring the severity of mental health symptoms in free-living contexts." (Page 6)

Suggestions for Improvement

• 💡 Explicitly Reiterate the Study's Core Contribution

  High impact. The conclusion effectively summarizes the 'what' (the findings) but could be strengthened by briefly reiterating the 'so what' (the study's core contribution). Explicitly connecting the results back to the research gap identified in the introduction—the challenge of moving psychophysiological measurement from the lab to the real world—would provide a more powerful and complete closing statement, reinforcing the paper's novelty.

  "Our results suggest ambulatory SCL measured from the dorsal wrist can be practical and useful in measuring the severity of mental health symptoms in free-living contexts." (Page 6)

  Implementation: Revise the final sentence to more directly frame the study's contribution. For example, change 'Our results suggest ambulatory SCL measured from the dorsal wrist can be practical and useful...' to 'Our results demonstrate that ambulatory SCL, measured via a commercial smartwatch, is a practical and useful tool for capturing physiological correlates of mental health symptoms, bridging a critical gap between laboratory findings and their application in free-living contexts.'

• 💡 Frame the 'Early Morning' Finding as a Future Direction

  Medium impact. The conclusion highlights the 'early morning' EDA difference as a key finding. However, the limitations section notes that sensor data quality is lower during this period due to deactivation during sleep. To enhance transparency and scientific rigor, the conclusion could briefly frame this compelling finding as a crucial area for future investigation, thereby acknowledging the current technical constraints while simultaneously pointing toward a clear next step for the field.

  "The most prominent temporal difference between participants with clinically elevated symptoms and healthy controls was EDA measurements that occurred in the early morning." (Page 6)

  Implementation: Modify the sentence about the temporal difference to frame it as a forward-looking point. For instance: 'The most prominent temporal difference... was EDA measurements that occurred in the early morning, highlighting this period as a critical target for future research with technologies capable of continuous overnight sensing.'